In the context of the European Space Agency/European Space Operations Centre funded Study “GNSS Contribution to Next Generation Global Ionospheric Monitoring,” four ionospheric models based on GNSS data (the Electron Density Assimilative Model, EDAM; the Ionosphere Monitoring Facility, IONMON v2; the Tomographic Ionosphere model, TOMION; and the Neustrelitz TEC Models, NTCM) have been run using a controlled set of input data. Each model output has been tested against differential slant TEC (dSTEC) truth data for high (May 2002) and low (December 2006) sunspot periods. Three of the models (EDAM, TOMION, and NTCM) produce dSTEC standard deviation results that are broadly consistent with each other and with standard deviation spreads of ∼1 TECu for December 2006 and ∼1.5 TECu for May 2002. The lowest reported standard deviation across all models and all stations was 0.99 TECu (EDAM, TLSE station for December 2006 night). However, the model with the best overall dSTEC performance was TOMION which has the lowest standard deviation in 28 out of 52 test cases (13 stations, two test periods, day and night). This is probably related to the interpolation techniques used in TOMION exploiting the spatial stationarity of vertical TEC error decorrelation.
Plasmaspheric electron content is, beyond the ionosphere as major source, a significant contributor to the overall TEC budget affecting GNSS signals. The plasmasphere can induce half or more of the GNSS range errors caused by atmospheric electrical charges, in particular at nighttime. At DLR Neustrelitz, Germany, GPS measurements recorded onboard the LEO satellite CHAMP were used to reconstruct the topside electron density distribution (ionosphere and plasmasphere) up to GPS altitude, applying a model-based assimilation technique. In this paper, the potential of these CHAMP topside reconstructions for analyzing space weather related changes in the geo-plasma is investigated. For this purpose, comparisons are made between the CHAMP reconstructed profiles and electron densities derived from passive radio wave observations by the IMAGE RPI instrument for years 2001 till 2005.The comparison results indicate that an improvement, compared to the electron density of a background model, can be achieved by CHAMP data assimilation. The improvement is especially visible in the L-shell region below 3, which contributes notably to the GNSS signal delays. However, for the region around the plasmapause, systematical electron density underestimations of the background model w.r.t. the IMAGE data are detected. The rather limited CHAMP data coverage and the degraded observation geometry at these high altitudes seem to be not sufficient for complete compensation of this underestimation during the assimilation procedure.The results presented in this paper demonstrate the strengths of LEO TEC data assimilation, but at the same time illustrate the necessity to improve the modeling of the plasmasphere region above 4 ER L-shell distances. Furthermore, they reveal the need of additional data to establish an appropriate data base for the modeling of the complete plasmasphere.
-Context: Calibration of radiometric tracking data for effects in the Earth atmosphere is a crucial element in the field of deep-space orbit determination (OD). The troposphere can induce propagation delays in the order of several meters, the ionosphere up to the meter level for X-band signals and up to tens of meters, in extreme cases, for L-band ones. The use of media calibrations based on Global Navigation Satellite Systems (GNSS) measurement data can improve the accuracy of the radiometric observations modelling and, as a consequence, the quality of orbit determination solutions. Aims: ESOC Flight Dynamics employs ranging, Doppler and delta-DOR (Delta-Differential One-Way Ranging) data for the orbit determination of interplanetary spacecraft. Currently, the media calibrations for troposphere and ionosphere are either computed based on empirical models or, under mission specific agreements, provided by external parties such as the Jet Propulsion Laboratory (JPL) in Pasadena, California, USA. In order to become independent from external models and sources, decision fell to establish a new in-house internal service to create these media calibrations based on GNSS measurements recorded at the ESA tracking sites and processed in-house by the ESOC Navigation Support Office with comparable accuracy and quality. Methods: For its concept, the new service was designed to be as much as possible depending on own data and resources and as less as possible depending on external models and data. Dedicated robust and simple algorithms, well suited for operational use, were worked out for that task. This paper describes the approach built up to realize this new in-house internal media calibration service. Results: Test results collected during three months of running the new media calibrations in quasioperational mode indicate that GNSS-based tropospheric corrections can remove systematic signatures from the Doppler observations and biases from the range ones. For the ionosphere, a direct way of verification was not possible due to non-availability of independent third party data for comparison. Nevertheless, the tests for ionospheric corrections showed also slight improvements in the tracking data modelling, but not to an extent as seen for the tropospheric corrections. Conclusions: The validation results confirmed that the new approach meets the requirements upon accuracy and operational use for the tropospheric part, while some improvement is still ongoing for the ionospheric one. Based on these test results, green light was given to put the new in-house service for media calibrations into full operational mode in April 2017.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.